FigureYa52 GOplot

小丫微信: epigenomics E-mail: figureya@126.com

作者：小丫

Y叔编辑校验

需求描述

用GOplot展示clusterProfiler的富集分析结果，画出paper里的⭕️图

出自https://www.nature.com/articles/s41467-017-01195-y

出自https://www.karger.com/Article/FullText/492519

应用场景

适用于：既想用clusterProfiler做富集分析，又想用GOplot展示结果，但是不知道二者怎样衔接的小伙伴。

此处以GO为例，获得clusterProfiler的富集分析结果，生成GOplot所需的格式，用GOplot画⭕️图。

clusterProfiler除了擅长做GO富集分析以外，还可以用KEGG、Diseaes、Reactome、DAVID、MSigDB等注释库做富集分析。另外，enrichplot自带的gseaplot2函数可以生成GSEA结果的矢量图、多条pathway画到同一个图上，完美代替Java版本的GSEA，看这篇：https://mp.weixin.qq.com/s/AamfRz0BUENCi_1P0P-Ruw。有关clusterProfiler、enrichplot本身的问题，建议加入Y叔知识星球提问。

环境设置

安装需要的包

#使用国内镜像安装包
options("repos"= c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
options(BioC_mirror="http://mirrors.ustc.edu.cn/bioc/")

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("clusterProfiler", version = "3.8")
BiocManager::install("org.Mm.eg.db", version = "3.8")

install.packages('GOplot')

加载需要用到的包

library(clusterProfiler)

## Warning: package 'clusterProfiler' was built under R version 3.5.1

## 

## clusterProfiler v3.10.0  For help: https://guangchuangyu.github.io/software/clusterProfiler
## 
## If you use clusterProfiler in published research, please cite:
## Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

library(AnnotationHub)

## Warning: package 'AnnotationHub' was built under R version 3.5.1

## Loading required package: BiocGenerics

## Warning: package 'BiocGenerics' was built under R version 3.5.1

## Loading required package: parallel

## 
## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
## 
##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
## 
##     IQR, mad, sd, var, xtabs

## The following objects are masked from 'package:base':
## 
##     anyDuplicated, append, as.data.frame, basename, cbind,
##     colMeans, colnames, colSums, dirname, do.call, duplicated,
##     eval, evalq, Filter, Find, get, grep, grepl, intersect,
##     is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
##     paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
##     Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
##     table, tapply, union, unique, unsplit, which, which.max,
##     which.min

library(AnnotationDbi)

## Warning: package 'AnnotationDbi' was built under R version 3.5.1

## Loading required package: stats4

## Loading required package: Biobase

## Warning: package 'Biobase' was built under R version 3.5.1

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## 
## Attaching package: 'Biobase'

## The following object is masked from 'package:AnnotationHub':
## 
##     cache

## Loading required package: IRanges

## Warning: package 'IRanges' was built under R version 3.5.1

## Loading required package: S4Vectors

## Warning: package 'S4Vectors' was built under R version 3.5.1

## 
## Attaching package: 'S4Vectors'

## The following object is masked from 'package:base':
## 
##     expand.grid

library(GOplot)

## Loading required package: ggplot2

## Loading required package: ggdendro

## Loading required package: gridExtra

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:Biobase':
## 
##     combine

## The following object is masked from 'package:BiocGenerics':
## 
##     combine

## Loading required package: RColorBrewer

library(ggplot2)

Sys.setenv(LANGUAGE = "en") #显示英文报错信息
options(stringsAsFactors = FALSE) #禁止chr转成factor

获取ENTREZ ID跟基因名的对应关系

如果你的基因ID已经是ENTREZ ID，并保存成very_easy_input_**.csv的格式，就可以跳过这步，直接进入“富集分析”

根据基因名gene symbol找到相应的ensembl ID。模式生物和非模式生物的转换代码稍有不同，根据自己的物种，选择运行以下两段之一。

人和模式生物ENTREZ ID的获取

此处以例文中的小鼠数据为例，其他18个物种只需更改OrgDb = "org.Mm.eg.db"参数

具体物种对应的R包名字看这页：http://bioconductor.org/packages/release/BiocViews.html#___OrgDb

如果你关心的物种不在这个列表里，请跳过这步，直接进入“非模式生物ENTREZ ID的获取”

gsym.fc <- read.csv("easy_input_Mm.csv", as.is = T)
dim(gsym.fc)

## [1] 201   2

#查看有哪些ID
#keytypes(org.Mm.eg.db)
gsym.id <- bitr(gsym.fc$SYMBOL, #基因名
                fromType = "SYMBOL", #从gene symbol
                toType = "ENTREZID", #提取ENTREZ ID
                OrgDb = "org.Mm.eg.db") #相应物种的包，人是org.Hs.eg.db

## Warning in bitr(gsym.fc$SYMBOL, fromType = "SYMBOL", toType = "ENTREZID", :
## 1.49% of input gene IDs are fail to map...

head(gsym.id)

##    SYMBOL ENTREZID
## 1    Atrn    11990
## 2 Cacna1e    12290
## 3 Cacna1g    12291
## 4   Capn2    12334
## 5   Cdh11    12552
## 6    Cdh4    12561

#合并基因名、ENTREZID、foldchange
idvec <- gsym.id$ENTREZID
names(idvec) <- gsym.id$SYMBOL
gsym.fc$ENTREZID <- idvec[gsym.fc$SYMBOL]
head(gsym.fc)

##    SYMBOL    log2fc ENTREZID
## 1    Atrn -0.308724    11990
## 2 Cacna1e -0.352114    12290
## 3 Cacna1g -0.300020    12291
## 4   Capn2 -0.199424    12334
## 5   Cdh11 -0.206564    12552
## 6    Cdh4 -0.500161    12561

#保存到文件
write.csv(gsym.fc[,c(3,2)], "very_easy_input_Mm.csv", quote = F, row.names = F)

非模式生物ENTREZ ID的获取

模式生物请跳过这段，直接进入“富集分析”。

参考资料：

• Y叔公众号biobabble：https://mp.weixin.qq.com/s/lHKZtzpN2k9uPN7e6HjH3w

• AnnotationHub包的说明文档：https://bioconductor.org/packages/release/bioc/vignettes/AnnotationHub/inst/doc/AnnotationHub.html

准备工作：

把基因名跟ENTREZ ID对应关系的注释文件保存到本地文件，以后想要获取同一基因组版本的ENTREZ ID时，直接导入这个“Zmays(物种名).AH66225(版本).sqlite”文件就可以了。此处以玉米为例：

hub <- AnnotationHub() #大概需要2分钟，网速慢就要更久

#查看AnnotationHub里有哪些物种，记住idx列里的AH***
#d <- display(hub) 

#或者直接搜zea(玉米拉丁名的一部分)
query(hub, "zea") 

#此处下载“AH61838”
maize.db <- hub[['AH66225']] #大概需要3分钟，网速慢就要更久

#查看包含的基因数
length(keys(maize.db)) 
#查看包含多少种ID
columns(maize.db)
#查看前几个基因的ID长啥样
select(maize.db, keys(maize.db)[1:3], 
       c("REFSEQ", "SYMBOL"), #你想获取的ID
       "ENTREZID")

#保存到文件
saveDb(maize.db, "Zmays.AH66225.sqlite")

根据基因名gene symbol获取ENTREZ ID

#读入差异基因
gsym.fc <- read.csv("easy_input_Zm.csv")
head(gsym.fc)

##           SYMBOL     log2fc
## 1 pco072676(750)  1.7600181
## 2           crr1  0.1031444
## 3           sqs1  1.3818824
## 4        umc2347 -1.5934269
## 5      LOC541617 -0.9625051
## 6           mpk2 -1.9493218

dim(gsym.fc)

## [1] 1000    2

#读入前面保存到本地的注释文件
maize.db <- loadDb("Zmays.AH66225.sqlite")

gsym.id <- bitr(gsym.fc$SYMBOL, #基因名所在的列
     "SYMBOL", #根据gene symbol
     "ENTREZID", #提取ENTREZ ID
     maize.db) #玉米注释

## 'select()' returned 1:many mapping between keys and columns

head(gsym.id)

##           SYMBOL ENTREZID
## 1 pco072676(750)   541612
## 2           crr1   541613
## 3           sqs1   541614
## 4        umc2347   541615
## 5      LOC541617   541617
## 6           mpk2   541618

dim(gsym.id)

## [1] 1006    2

#合并基因名、ENTREZID、foldchange
idvec <- gsym.id$ENTREZID
names(idvec) <- gsym.id$SYMBOL
gsym.fc$ENTREZID <- idvec[gsym.fc$SYMBOL]
head(gsym.fc)

##           SYMBOL     log2fc ENTREZID
## 1 pco072676(750)  1.7600181   541612
## 2           crr1  0.1031444   541613
## 3           sqs1  1.3818824   541614
## 4        umc2347 -1.5934269   541615
## 5      LOC541617 -0.9625051   541617
## 6           mpk2 -1.9493218   541618

dim(gsym.fc)

## [1] 1000    3

#保存到文件
write.csv(gsym.fc[,c(3,2)], "very_easy_input_Zm.csv", quote = F, row.names = F)

富集分析

参考资料：http://bioconductor.org/packages/release/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html#supported-organisms

如果你已经做好了富集分析，并且保存成“enrichGO_output.csv”的格式，就可以跳过这部分，直接进入“把clusterProfiler输出的富集分析结果转成GOplot所需的格式”

#ENTREZ ID位于第一列，log2foldchange位于第二列
id.fc <- read.csv("very_easy_input_Mm.csv", as.is = T)
head(id.fc)

##   ENTREZID    log2fc
## 1    11990 -0.308724
## 2    12290 -0.352114
## 3    12291 -0.300020
## 4    12334 -0.199424
## 5    12552 -0.206564
## 6    12561 -0.500161

dim(id.fc)

## [1] 201   2

ego <- enrichGO(gene = id.fc$ENTREZID,
                #小鼠用这行
                OrgDb = org.Mm.eg.db,
                #人类用这行
                #OrgDb = org.Hs.eg.db,
                #非模式生物用这行，例如玉米
                #OrgDb = maize.db,
                ont = "BP", #或MF或CC
                pAdjustMethod = "BH",
                #pvalueCutoff  = 0.001,
                qvalueCutoff  = 0.01) 
dim(ego)

## [1] 169   9

#把富集分析结果保存到文件
write.csv(ego,"enrichGO_output.csv",quote = F)

富集的GO term有些是相似的，可以用语义学方法，合并相似的GO term。需要较长时间。

参考资料：https://guangchuangyu.github.io/2015/10/use-simplify-to-remove-redundancy-of-enriched-go-terms/

#ego2 <- simplify(ego, cutoff = 0.7, by = "p.adjust", select_fun = min)
#dim(ego2)
#write.csv(ego2,"enrichGO_simplify_output.csv",quote = F)

用enrichplot自带的函数画⭕️

参考资料：https://bioconductor.org/packages/release/bioc/vignettes/enrichplot/inst/doc/enrichplot.html

#把ENTREZ ID转为gene symbol
egox <- setReadable(ego, 'org.Mm.eg.db', #物种
                    'ENTREZID')

#把基因倍数信息转成画图所需的格式
geneList <- id.fc$log2fc
names(geneList) <- id.fc$ENTREZID

#画⭕️图
cnetplot(egox, 
         foldChange = geneList, 
         #foldChange = NULL, #不展示倍数
         circular = TRUE,
         #node_label = FALSE, #如果太多，就不要显示基因名了
         showCategory = 4, #显示富集的term数量，默认5
         colorEdge = TRUE)

#保存到pdf文件
ggsave("clusterProfiler_circle.pdf", width = 8, height = 5)

#不画成⭕️，process分开，效果更好呢
cnetplot(egox, 
         foldChange = geneList, 
         #foldChange = NULL, #不展示倍数
         #circular = TRUE,
         #node_label = FALSE, #不显示基因名
         showCategory = 4, #显示的富集term数量，默认5
         colorEdge = TRUE)

ggsave("clusterProfiler_not_circle.pdf", width = 8, height = 5)

把clusterProfiler输出的富集分析结果转成GOplot所需的格式

#读入富集分析结果
ego <- read.csv("enrichGO_output.csv", header = T)
ego[1,]

##            X         ID                 Description GeneRatio   BgRatio
## 1 GO:0046777 GO:0046777 protein autophosphorylation    14/194 235/23239
##         pvalue     p.adjust      qvalue
## 1 1.205327e-08 2.289313e-05 1.75051e-05
##                                                                                geneID
## 1 13869/14254/14255/16001/16337/16542/16589/17096/18750/15467/27103/53416/54635/16590
##   Count
## 1    14

go <- data.frame(Category = "BP",
                 ID = ego$ID,
                 Term = ego$Description, 
                 Genes = gsub("/", ", ", ego$geneID), 
                 adj_pval = ego$p.adjust)

#基因变化倍数
id.fc <- read.csv("very_easy_input_Mm.csv", as.is = T)
head(id.fc)

##   ENTREZID    log2fc
## 1    11990 -0.308724
## 2    12290 -0.352114
## 3    12291 -0.300020
## 4    12334 -0.199424
## 5    12552 -0.206564
## 6    12561 -0.500161

genelist <- data.frame(ID = id.fc$ENTREZID, logFC = id.fc$log2fc)             

#把富集分析和倍数整合在一起
circ <- circle_dat(go, genelist)
head(circ)

##   category         ID                        term count genes     logFC
## 1       BP GO:0046777 protein autophosphorylation    14 13869 -0.301868
## 2       BP GO:0046777 protein autophosphorylation    14 14254 -0.183248
## 3       BP GO:0046777 protein autophosphorylation    14 14255 -0.594008
## 4       BP GO:0046777 protein autophosphorylation    14 16001 -0.213881
## 5       BP GO:0046777 protein autophosphorylation    14 16337 -0.182849
## 6       BP GO:0046777 protein autophosphorylation    14 16542 -0.150017
##       adj_pval    zscore
## 1 2.289313e-05 -1.069045
## 2 2.289313e-05 -1.069045
## 3 2.289313e-05 -1.069045
## 4 2.289313e-05 -1.069045
## 5 2.289313e-05 -1.069045
## 6 2.289313e-05 -1.069045

#根据ENTREZ ID提取gene symbol
id.gsym <- bitr(circ$genes, #基因名
                fromType = "ENTREZID", #从ENTREZ ID
                toType = "SYMBOL", #提取gene symbol
                OrgDb = "org.Mm.eg.db") #相应物种的包

## 'select()' returned 1:1 mapping between keys and columns

#把circ里的ENTREZ ID换成gene symbol
rownames(id.gsym) <- id.gsym$ENTREZID
circ.gsym <- circ
circ.gsym$genes <- id.gsym[circ$genes,]$SYMBOL
head(circ.gsym)

##   category         ID                        term count genes     logFC
## 1       BP GO:0046777 protein autophosphorylation    14 Erbb4 -0.301868
## 2       BP GO:0046777 protein autophosphorylation    14  Flt1 -0.183248
## 3       BP GO:0046777 protein autophosphorylation    14  Flt3 -0.594008
## 4       BP GO:0046777 protein autophosphorylation    14 Igf1r -0.213881
## 5       BP GO:0046777 protein autophosphorylation    14  Insr -0.182849
## 6       BP GO:0046777 protein autophosphorylation    14   Kdr -0.150017
##       adj_pval    zscore
## 1 2.289313e-05 -1.069045
## 2 2.289313e-05 -1.069045
## 3 2.289313e-05 -1.069045
## 4 2.289313e-05 -1.069045
## 5 2.289313e-05 -1.069045
## 6 2.289313e-05 -1.069045

#现在就可以用GOplot画图了，例如
#GOBar(subset(circ, category == 'BP'))
#GOBubble(circ, labels = 3)
#GOCircle(circ)

用GOplot画⭕️图

参考资料：http://wencke.github.io/

准备画⭕️图所需的数据格式

#参数设置
n = 5 #圈图需要选定term，这里画前面5个

chord <- chord_dat(circ, genelist, go$Term[1:n])
head(chord)

##       protein autophosphorylation cell junction assembly
## 11990                           0                      0
## 12552                           0                      1
## 12561                           0                      1
## 12565                           0                      1
## 13052                           0                      0
## 13602                           0                      0
##       cell junction organization regulation of developmental growth
## 11990                          0                                  1
## 12552                          1                                  0
## 12561                          1                                  1
## 12565                          1                                  0
## 13052                          1                                  1
## 13602                          0                                  0
##       cell-cell adhesion via plasma-membrane adhesion molecules     logFC
## 11990                                                         0 -0.308724
## 12552                                                         1 -0.206564
## 12561                                                         1 -0.500161
## 12565                                                         1 -0.255698
## 13052                                                         1 -0.200243
## 13602                                                         1 -0.385248

#根据ENTREZ ID提取gene symbol
id.gsym <- bitr(row.names(chord), #基因名
                fromType = "ENTREZID", #从ENTREZ ID
                toType = "SYMBOL", #提取gene symbol
                OrgDb = "org.Mm.eg.db") #相应物种的包

## 'select()' returned 1:1 mapping between keys and columns

#把chord的列名ENTREZ ID换成gene symbol
rownames(id.gsym) <- id.gsym$ENTREZID
head(id.gsym)

##       ENTREZID  SYMBOL
## 11990    11990    Atrn
## 12552    12552   Cdh11
## 12561    12561    Cdh4
## 12565    12565    Cdh9
## 13052    13052   Cxadr
## 13602    13602 Sparcl1

chord.gsym <- chord
row.names(chord.gsym) <- id.gsym[row.names(chord),]$SYMBOL
head(chord.gsym)

##         protein autophosphorylation cell junction assembly
## Atrn                              0                      0
## Cdh11                             0                      1
## Cdh4                              0                      1
## Cdh9                              0                      1
## Cxadr                             0                      0
## Sparcl1                           0                      0
##         cell junction organization regulation of developmental growth
## Atrn                             0                                  1
## Cdh11                            1                                  0
## Cdh4                             1                                  1
## Cdh9                             1                                  0
## Cxadr                            1                                  1
## Sparcl1                          0                                  0
##         cell-cell adhesion via plasma-membrane adhesion molecules
## Atrn                                                            0
## Cdh11                                                           1
## Cdh4                                                            1
## Cdh9                                                            1
## Cxadr                                                           1
## Sparcl1                                                         1
##             logFC
## Atrn    -0.308724
## Cdh11   -0.206564
## Cdh4    -0.500161
## Cdh9    -0.255698
## Cxadr   -0.200243
## Sparcl1 -0.385248

用⭕️图展示每个term里的基因及其变化倍数

GOChord(chord.gsym, 
        space = 0.02, #基因方块间隙
        gene.order = 'logFC', 
        lfc.col = c('darkgoldenrod1', 'black', 'cyan1'), #自定义变化倍数的颜色
        gene.space = 0.25, #基因名跟⭕️的相对距离
        gene.size = 8, #基因名字体大小 
        border.size = 0.1, #中间曲线的黑色边的粗细
        process.label = 8) #term字体大小

## Warning: Using size for a discrete variable is not advised.

## Warning: Removed 5 rows containing missing values (geom_point).

ggsave("GOChord.pdf", width = 12, height = 14)

## Warning: Using size for a discrete variable is not advised.

## Warning: Removed 5 rows containing missing values (geom_point).

用⭕️聚类图展示相似变化趋势的基因所在的term

#定义足够多的颜色，后面从这里选颜色
mycol <- c("#223D6C","#D20A13","#FFD121","#088247","#58CDD9","#7A142C","#5D90BA","#431A3D","#91612D","#6E568C","#E0367A","#D8D155","#64495D","#7CC767")

GOCluster(circ.gsym, go$Term[1:n], 
          clust.by = 'logFC', #用变化倍数聚类
          #clust.by = 'term', 用富集的term聚类
          lfc.col = c('darkgoldenrod1', 'black', 'cyan1'), #自定义变化倍数的颜色
          lfc.space = 0.05, #倍数跟树间的空隙大小
          lfc.width = 0.01, #变化倍数的⭕️宽度
          term.col = mycol[1:n], #自定义term的颜色
          term.space = 0.05, #倍数跟term间的空隙大小
          term.width = 0.15) #富集term的⭕️宽度

## Warning: Using size for a discrete variable is not advised.

## Warning: Removed 5 rows containing missing values (geom_point).

ggsave("GOCluster.pdf", width = 12, height = 14)

## Warning: Using size for a discrete variable is not advised.

## Warning: Removed 5 rows containing missing values (geom_point).

sessionInfo()

## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS  10.14
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] org.Mm.eg.db_3.7.0     GOplot_1.0.2           RColorBrewer_1.1-2    
##  [4] gridExtra_2.3          ggdendro_0.1-20        ggplot2_3.1.0         
##  [7] AnnotationDbi_1.44.0   IRanges_2.16.0         S4Vectors_0.20.0      
## [10] Biobase_2.42.0         AnnotationHub_2.14.1   BiocGenerics_0.28.0   
## [13] clusterProfiler_3.10.0
## 
## loaded via a namespace (and not attached):
##  [1] enrichplot_1.2.0              bit64_0.9-7                  
##  [3] progress_1.2.0                httr_1.3.1                   
##  [5] rprojroot_1.3-2               UpSetR_1.3.3                 
##  [7] tools_3.5.0                   backports_1.1.2              
##  [9] R6_2.3.0                      DBI_1.0.0                    
## [11] lazyeval_0.2.1                colorspace_1.3-2             
## [13] withr_2.1.2                   tidyselect_0.2.5             
## [15] prettyunits_1.0.2             bit_1.1-14                   
## [17] compiler_3.5.0                xml2_1.2.0                   
## [19] labeling_0.3                  triebeard_0.3.0              
## [21] scales_1.0.0                  ggridges_0.5.1               
## [23] stringr_1.3.1                 digest_0.6.18                
## [25] rmarkdown_1.10                DOSE_3.8.0                   
## [27] pkgconfig_2.0.2               htmltools_0.3.6              
## [29] rlang_0.3.0.1                 rstudioapi_0.8               
## [31] RSQLite_2.1.1                 shiny_1.2.0                  
## [33] bindr_0.1.1                   gridGraphics_0.3-0           
## [35] farver_1.0                    jsonlite_1.5                 
## [37] BiocParallel_1.16.0           GOSemSim_2.8.0               
## [39] dplyr_0.7.7                   magrittr_1.5                 
## [41] ggplotify_0.0.3               GO.db_3.7.0                  
## [43] Matrix_1.2-15                 Rcpp_1.0.0                   
## [45] munsell_0.5.0                 viridis_0.5.1                
## [47] stringi_1.2.4                 yaml_2.2.0                   
## [49] ggraph_1.0.2                  MASS_7.3-51.1                
## [51] plyr_1.8.4                    qvalue_2.14.0                
## [53] grid_3.5.0                    blob_1.1.1                   
## [55] promises_1.0.1                ggrepel_0.8.0                
## [57] DO.db_2.9                     crayon_1.3.4                 
## [59] lattice_0.20-38               cowplot_0.9.3                
## [61] splines_3.5.0                 hms_0.4.2                    
## [63] knitr_1.20                    pillar_1.3.0                 
## [65] fgsea_1.8.0                   igraph_1.2.2                 
## [67] reshape2_1.4.3                fastmatch_1.1-0              
## [69] glue_1.3.0                    evaluate_0.12                
## [71] data.table_1.11.8             BiocManager_1.30.3           
## [73] httpuv_1.4.5                  tweenr_1.0.0                 
## [75] urltools_1.7.1                gtable_0.2.0                 
## [77] purrr_0.2.5                   tidyr_0.8.2                  
## [79] assertthat_0.2.0              ggforce_0.1.3                
## [81] mime_0.6                      europepmc_0.3                
## [83] xtable_1.8-3                  later_0.7.5                  
## [85] viridisLite_0.3.0             tibble_1.4.2                 
## [87] rvcheck_0.1.1                 memoise_1.1.0                
## [89] units_0.6-1                   bindrcpp_0.2.2               
## [91] interactiveDisplayBase_1.20.0